• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.96-97
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• 2017

When a new student, a guest, or a student who first visited a school wants to know information about the school, he / she searches through a smartphone or a tablet. However, if you visit the homepage of the school, you do not know exactly where the information you want to find is located, and you spend a lot of time. In this paper, we develop a school guidance system using Alexa with speech recognition function. Divide the school guidance system into college introduction, major, college activities, and entrance information topics, and fill in the details by topic. In the lambda function of Amazon Web Services, we use Node.js to create information on topics and provide information to users by voice.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.75-84
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• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.